data assimilation schemes in numerical weather forecasting and their link with ensemble forecasting...
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Data assimilation schemes in numerical weather forecasting
and their link with ensemble forecasting
Gérald Desroziers
Météo-France, Toulouse, France
Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
Global Arpège model : DX ~ 15 km
Numerical Weather Prediction at Météo-France
DX ~ 10 km
Arome : DX ~ 2,5 km
Initial condition problem
Observations yo
État atmosphérique à t0 Prévision état à t0 + h
Ebauche xb = M (xa -)
Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
Data coverage
05/09/03 09–15 UTC(courtesy J-.N. Thépaut)
Radiosondes Pilots and profilers Aircraft
Synops and ships Buoys
ATOVS Satobs Geo radiances
ScatterometerSSM/I Ozone
Satellites
(EUMETSAT)
0
5
10
15
20
25
30
35
40
45
50
55
No. of sources
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Year
Number of satellite sources used at ECMWF
AEOLUSSMOSTRMMCHAMP/GRACECOSMICMETOPMTSAT radMTSAT windsJASONGOES radMETEOSAT radGMS windsGOES windsMETEOSAT windsAQUATERRAQSCATENVISATERSDMSPNOAA
Satellite data sources
(courtesy J-.N.Thépaut, ECMWF)
General formalism Statistical linear estimation :
xa = xb + x =xb + K d = xb + BHT (HBHT+R)-1 d,
with d = yo – H (xb ), innovation, K, gain matrix, B et R, covariances of background and observation errors,
H is called « observation operator » (Lorenc, 1986),
It is most often explicit,
It can be non-linear (satellite observations)
It can include an error,
Variational schemes require linearized and adjoint observation operators,
4D-Var generalizes the notion of « observation operator » .
Statistical hypotheses
Observations are supposed un-biased: E(o) = 0.
If not, they have to be preliminarly de-biased,
or de-biasing can be made along the minimization (Derber and Wu, 1998; Dee, 2005; Auligné, 2007).
Oservation error variances are supposed to be known ( diagonal elements of R = E(ooT) ).
Observation errors are supposed to be un-correlated : ( non-diagonal elements of E(ooT) = 0 ),
but, the representation of observation error correlations is also investigated (Fisher, 2006) .
Implementation
Variational formulation: minimization of J(x) = xT B-1 x + (d-H x)T R-1 (d-H x)
Computation of J’: development and use of adjoint operators
4D-Var : generalized observation operator H : addition of forecast model
M.
Cost reduction : low resolution increment x (Courtier, Thépaut et Hollingsworth, 1994)
9h 12h 15h
Assimilation window
JbJo
Jo
Jo
obs
obs
obs
analysis
xa
xb correctedforecast
« old »forecast
4D-Var : principle
Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
A posteriori diagnostics
Is the system consistent?
We should have E[J(xa) ] = p,
p = total number of observations, but also
E[Joi(xa) ] = pi – Tr(Ri-1/2 H
i A Hi
T Ri-1/2 ),
pi : number of observations associated with Joi
(Talagrand, 1999) .
Computation of optimal E[Joi(xa) ] by a Monte-Carlo procedure is possible. (Desroziers et Ivanov, 2001) .
Application : optimisation of R
(Chapnik, et al, 2004; Buehner, 2005)
Optimisation of HIRS o
One tries to obtain
E[Joi (xa)] = (E[Joi (xa)])opt.
by adjusting the oi
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Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
Ensemble of perturbed analyses
Simulation of the estimation errors
along analyses and forecasts.
Documentation of error covariances
– over a long period (a month/ a season),
– for a particular day.
(Evensen, 1997; Fisher, 2004; Berre et al, 2007)
Ensembles Based on a perturbation of observations
The same analysis equation and (sub-optimal) operators K and H
are involved in the equations of xa and a:
xa = (I – KH) xb + K xo
a = (I – KH) b + K o
The same equation also holds for the analysis perturbation:
pa = (I – KH) pb + K po
Background error standard-deviations
Over a month
Vorticity at 500 hPa
For a particular date08/12/2006 00H
Vorticity at 500 hPa
500 hPa vorticity error surface pressure
Ensemble assimilation:errors 08/12/2006 06UTC
850 hPa vorticity error (shaded)
sea surface level pressure (isoligns)
Ensemble assimilation:errors 15/02/2008 12UTC
(Montroty, 2008)
Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
Measure of the impact of observations
Total reduction of estimation error variance:r = Tr(K H B)
Reduction due to observation set i :ri = Tr(Ki Hi B)
Variance reduction normalized by B :ri
DFS = Tr(Ki Hi)
Reduction of error projected onto a variable/area:ri
P = Tr(P Ki Hi B PT)
Reduction of error evolved by a forecast model:ri
PM = Tr(P M Ki Hi B MT PT) = Tr(L Ki Hi B LT)
(Cardinali, 2003; Fisher, 2003; Chapnik et al, 2006)
Randomized estimates of error reduction on analyses and forecasts
)( LBHKL Tii trr
It can be shown that
).( KLLBH Titr
This can be estimated by a randomization procedure:
joT
ji
Tj
oi iir )()( 1 yKLLBHRy
where jo)( y is a vector of observation perturbations and
ja)( x the corresponding perturbation on the analysis.
ja
iij
jo LLBBHR )()( '*2/12/11 xy
(Fisher, 2003; Desroziers et al, 2005)
Degree of Freedom for Signal (DFS)
01/06/2008 00H
Error variance reduction
% of error variance reduction for T 850 hPaby area and observation type
(Desroziers et al, 2005)
Outline
Numerical weather prediction
Data assimilation
A posteriori diagnostics: optimizing error statistics
Ensemble assimilation
Impact of observations on analyses and forecasts
Conclusion and perspectives
Conclusion and perspectives
Importance of the notion of « observation operator » :- most often explicit,- rarely statistical
Large size problems :- state vector : ~ 10^7- observations : ~ 10^6
Ensemble assimilation:– estimation error covariances– measure of the impact of observations– link with Ensemble forecasting (~ 40 members of +96h forecasts)